code: def predict_mask(self, sequence, model, tokenizer, top_n=2):
      input = tokenizer.encode(sequence, return_tensors="tf")
      mask_token_index = tf.where(input == tokenizer.mask_token_id)[
          0, 1
      ]
      token_logits = model(input)[0]
      mask_token_logits = token_logits[0, mask_token_index, :]
  
      probabilities = tf.nn.softmax(mask_token_logits)
      topk = tf.math.top_k(probabilities, top_n)
      top_n_probs, top_n_tokens = (
          topk.values.numpy(),
          topk.indices.numpy(),
      )
      results = [
          {
              "token": tokenizer.decode([top_n_tokens[i]]),
              "probability": float(top_n_probs[i]),
          }
          for i in range(len(top_n_probs))
      ]
      # print(results)
      return results
  
file_path: neuralqa/neuralqa/expander/mlmexpander.py
imports: from neuralqa.expander import Expander
  import logging
  from transformers import AutoTokenizer, TFBertForMaskedLM
  import tensorflow
  import time
  import spacy
label_desc: Use image node_id 2 for calling the MLMExpander obj's underlying member method code with example usage: obj.predict_mask(sequence, model, tokenizer, top_n) and returns: results